Overview

Brought to you by YData

Dataset statistics

Number of variables59
Number of observations505354
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory227.5 MiB
Average record size in memory472.0 B

Variable types

Numeric15
Categorical44

Alerts

Aspect is highly overall correlated with Hillshade_3pmHigh correlation
Avg_Hillshade is highly overall correlated with Hillshade_3pm and 2 other fieldsHigh correlation
Cover_Type is highly overall correlated with Soil_Type_9 and 1 other fieldsHigh correlation
Distance_to_Water is highly overall correlated with Horizontal_Distance_To_Hydrology and 1 other fieldsHigh correlation
Elevation is highly overall correlated with Soil_Type_39 and 2 other fieldsHigh correlation
Elevation_x_Slope is highly overall correlated with SlopeHigh correlation
Hillshade_3pm is highly overall correlated with Aspect and 3 other fieldsHigh correlation
Hillshade_9am is highly overall correlated with Hillshade_3pmHigh correlation
Hillshade_Noon is highly overall correlated with Avg_Hillshade and 1 other fieldsHigh correlation
Horizontal_Distance_To_Fire_Points is highly overall correlated with Hydro_Road_Fire_DistanceHigh correlation
Horizontal_Distance_To_Hydrology is highly overall correlated with Distance_to_Water and 1 other fieldsHigh correlation
Horizontal_Distance_To_Roadways is highly overall correlated with Hydro_Road_Fire_DistanceHigh correlation
Hydro_Road_Fire_Distance is highly overall correlated with Horizontal_Distance_To_Fire_Points and 3 other fieldsHigh correlation
Slope is highly overall correlated with Avg_Hillshade and 1 other fieldsHigh correlation
Soil_Type_28 is highly overall correlated with Wilderness_Area_0High correlation
Soil_Type_39 is highly overall correlated with ElevationHigh correlation
Soil_Type_9 is highly overall correlated with Cover_Type and 2 other fieldsHigh correlation
Vertical_Distance_To_Hydrology is highly overall correlated with Distance_to_Water and 1 other fieldsHigh correlation
Wilderness_Area_0 is highly overall correlated with Hydro_Road_Fire_Distance and 2 other fieldsHigh correlation
Wilderness_Area_2 is highly overall correlated with Wilderness_Area_0High correlation
Wilderness_Area_3 is highly overall correlated with Cover_Type and 3 other fieldsHigh correlation
Wilderness_Area_1 is highly imbalanced (87.8%) Imbalance
Wilderness_Area_3 is highly imbalanced (62.2%) Imbalance
Soil_Type_0 is highly imbalanced (94.7%) Imbalance
Soil_Type_1 is highly imbalanced (92.1%) Imbalance
Soil_Type_2 is highly imbalanced (95.5%) Imbalance
Soil_Type_3 is highly imbalanced (88.7%) Imbalance
Soil_Type_4 is highly imbalanced (96.9%) Imbalance
Soil_Type_5 is highly imbalanced (90.0%) Imbalance
Soil_Type_6 is highly imbalanced (99.7%) Imbalance
Soil_Type_7 is highly imbalanced (99.5%) Imbalance
Soil_Type_8 is highly imbalanced (97.7%) Imbalance
Soil_Type_9 is highly imbalanced (70.0%) Imbalance
Soil_Type_10 is highly imbalanced (88.0%) Imbalance
Soil_Type_11 is highly imbalanced (67.5%) Imbalance
Soil_Type_12 is highly imbalanced (82.4%) Imbalance
Soil_Type_13 is highly imbalanced (99.1%) Imbalance
Soil_Type_14 is highly imbalanced (> 99.9%) Imbalance
Soil_Type_15 is highly imbalanced (95.4%) Imbalance
Soil_Type_16 is highly imbalanced (94.5%) Imbalance
Soil_Type_17 is highly imbalanced (96.5%) Imbalance
Soil_Type_18 is highly imbalanced (94.2%) Imbalance
Soil_Type_19 is highly imbalanced (87.5%) Imbalance
Soil_Type_20 is highly imbalanced (98.2%) Imbalance
Soil_Type_21 is highly imbalanced (71.0%) Imbalance
Soil_Type_22 is highly imbalanced (54.3%) Imbalance
Soil_Type_23 is highly imbalanced (78.8%) Imbalance
Soil_Type_24 is highly imbalanced (> 99.9%) Imbalance
Soil_Type_25 is highly imbalanced (95.8%) Imbalance
Soil_Type_26 is highly imbalanced (98.8%) Imbalance
Soil_Type_27 is highly imbalanced (98.0%) Imbalance
Soil_Type_29 is highly imbalanced (67.4%) Imbalance
Soil_Type_30 is highly imbalanced (72.2%) Imbalance
Soil_Type_31 is highly imbalanced (55.7%) Imbalance
Soil_Type_32 is highly imbalanced (64.5%) Imbalance
Soil_Type_33 is highly imbalanced (98.5%) Imbalance
Soil_Type_34 is highly imbalanced (97.3%) Imbalance
Soil_Type_35 is highly imbalanced (99.7%) Imbalance
Soil_Type_36 is highly imbalanced (99.3%) Imbalance
Soil_Type_37 is highly imbalanced (82.2%) Imbalance
Soil_Type_38 is highly imbalanced (85.5%) Imbalance
Soil_Type_39 is highly imbalanced (89.7%) Imbalance
Horizontal_Distance_To_Hydrology has 21630 (4.3%) zeros Zeros
Vertical_Distance_To_Hydrology has 34287 (6.8%) zeros Zeros
Distance_to_Water has 21630 (4.3%) zeros Zeros

Reproduction

Analysis started2025-06-09 15:35:44.297973
Analysis finished2025-06-09 15:37:22.361139
Duration1 minute and 38.06 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Elevation
Real number (ℝ)

High correlation 

Distinct1978
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2951.5881
Minimum1859
Maximum3858
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T18:37:22.442485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1859
5-th percentile2381
Q12814
median2987
Q33142
95-th percentile3322
Maximum3858
Range1999
Interquartile range (IQR)328

Descriptive statistics

Standard deviation275.48424
Coefficient of variation (CV)0.093334245
Kurtosis1.0931128
Mean2951.5881
Median Absolute Deviation (MAD)163
Skewness-0.89179409
Sum1.4915968 × 109
Variance75891.569
MonotonicityNot monotonic
2025-06-09T18:37:22.561138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2968 1625
 
0.3%
2962 1613
 
0.3%
2991 1613
 
0.3%
2972 1609
 
0.3%
2978 1603
 
0.3%
2975 1599
 
0.3%
2988 1554
 
0.3%
2955 1524
 
0.3%
2965 1519
 
0.3%
2952 1514
 
0.3%
Other values (1968) 489581
96.9%
ValueCountFrequency (%)
1859 1
 
< 0.1%
1860 1
 
< 0.1%
1861 1
 
< 0.1%
1863 1
 
< 0.1%
1866 1
 
< 0.1%
1867 1
 
< 0.1%
1868 1
 
< 0.1%
1871 3
< 0.1%
1872 4
< 0.1%
1873 1
 
< 0.1%
ValueCountFrequency (%)
3858 2
 
< 0.1%
3857 1
 
< 0.1%
3856 1
 
< 0.1%
3853 1
 
< 0.1%
3852 1
 
< 0.1%
3851 2
 
< 0.1%
3850 1
 
< 0.1%
3849 4
< 0.1%
3848 1
 
< 0.1%
3846 6
< 0.1%

Aspect
Real number (ℝ)

High correlation 

Distinct361
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean154.78752
Minimum0
Maximum360
Zeros4536
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T18:37:22.657411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11
Q157
median124
Q3263
95-th percentile344
Maximum360
Range360
Interquartile range (IQR)206

Descriptive statistics

Standard deviation112.6629
Coefficient of variation (CV)0.72785521
Kurtosis-1.2289254
Mean154.78752
Median Absolute Deviation (MAD)84
Skewness0.41649173
Sum78222490
Variance12692.929
MonotonicityNot monotonic
2025-06-09T18:37:22.757578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45 5854
 
1.2%
0 4536
 
0.9%
90 4111
 
0.8%
135 3443
 
0.7%
63 3320
 
0.7%
315 3236
 
0.6%
18 3077
 
0.6%
72 3071
 
0.6%
27 3071
 
0.6%
34 2521
 
0.5%
Other values (351) 469114
92.8%
ValueCountFrequency (%)
0 4536
0.9%
1 1506
 
0.3%
2 1709
 
0.3%
3 1718
 
0.3%
4 2023
0.4%
5 1844
0.4%
6 1981
0.4%
7 1951
0.4%
8 1983
0.4%
9 2229
0.4%
ValueCountFrequency (%)
360 49
 
< 0.1%
359 1220
0.2%
358 1566
0.3%
357 1643
0.3%
356 1801
0.4%
355 1725
0.3%
354 1784
0.4%
353 1732
0.3%
352 1782
0.4%
351 1938
0.4%

Slope
Real number (ℝ)

High correlation 

Distinct67
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.767395
Minimum0
Maximum66
Zeros621
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T18:37:22.873856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q18
median13
Q318
95-th percentile28
Maximum66
Range66
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.4737982
Coefficient of variation (CV)0.5428622
Kurtosis0.75038989
Mean13.767395
Median Absolute Deviation (MAD)5
Skewness0.85685891
Sum6957408
Variance55.857659
MonotonicityNot monotonic
2025-06-09T18:37:22.999438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 30511
 
6.0%
11 30123
 
6.0%
12 29324
 
5.8%
9 29017
 
5.7%
13 28187
 
5.6%
8 27610
 
5.5%
14 25932
 
5.1%
15 24691
 
4.9%
7 24304
 
4.8%
6 22768
 
4.5%
Other values (57) 232887
46.1%
ValueCountFrequency (%)
0 621
 
0.1%
1 3485
 
0.7%
2 7301
 
1.4%
3 10951
 
2.2%
4 15310
3.0%
5 19417
3.8%
6 22768
4.5%
7 24304
4.8%
8 27610
5.5%
9 29017
5.7%
ValueCountFrequency (%)
66 1
 
< 0.1%
65 2
 
< 0.1%
64 1
 
< 0.1%
63 1
 
< 0.1%
62 2
 
< 0.1%
61 4
< 0.1%
60 2
 
< 0.1%
59 3
< 0.1%
58 1
 
< 0.1%
57 7
< 0.1%

Horizontal_Distance_To_Hydrology
Real number (ℝ)

High correlation  Zeros 

Distinct551
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean266.15709
Minimum0
Maximum1397
Zeros21630
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T18:37:23.120955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30
Q1108
median216
Q3379
95-th percentile674
Maximum1397
Range1397
Interquartile range (IQR)271

Descriptive statistics

Standard deviation211.08873
Coefficient of variation (CV)0.79309826
Kurtosis1.6070835
Mean266.15709
Median Absolute Deviation (MAD)131
Skewness1.188776
Sum1.3450355 × 108
Variance44558.451
MonotonicityNot monotonic
2025-06-09T18:37:23.222064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 29993
 
5.9%
0 21630
 
4.3%
150 18274
 
3.6%
60 16778
 
3.3%
67 13454
 
2.7%
42 12960
 
2.6%
108 12702
 
2.5%
85 12156
 
2.4%
90 9757
 
1.9%
120 9343
 
1.8%
Other values (541) 348307
68.9%
ValueCountFrequency (%)
0 21630
4.3%
30 29993
5.9%
42 12960
2.6%
60 16778
3.3%
67 13454
2.7%
85 12156
2.4%
90 9757
 
1.9%
95 8120
 
1.6%
108 12702
2.5%
120 9343
 
1.8%
ValueCountFrequency (%)
1397 1
< 0.1%
1390 2
< 0.1%
1383 2
< 0.1%
1382 1
< 0.1%
1376 1
< 0.1%
1371 1
< 0.1%
1370 1
< 0.1%
1369 1
< 0.1%
1368 2
< 0.1%
1361 2
< 0.1%

Vertical_Distance_To_Hydrology
Real number (ℝ)

High correlation  Zeros 

Distinct692
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.002424
Minimum-159
Maximum601
Zeros34287
Zeros (%)6.8%
Negative45771
Negative (%)9.1%
Memory size3.9 MiB
2025-06-09T18:37:23.311581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-159
5-th percentile-7
Q17
median29
Q367
95-th percentile164
Maximum601
Range760
Interquartile range (IQR)60

Descriptive statistics

Standard deviation57.90111
Coefficient of variation (CV)1.2586535
Kurtosis5.8017203
Mean46.002424
Median Absolute Deviation (MAD)26
Skewness1.8979445
Sum23247509
Variance3352.5386
MonotonicityNot monotonic
2025-06-09T18:37:23.413887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 34287
 
6.8%
3 8388
 
1.7%
10 8084
 
1.6%
7 7807
 
1.5%
13 7707
 
1.5%
6 7694
 
1.5%
4 7529
 
1.5%
5 6717
 
1.3%
16 6715
 
1.3%
23 6539
 
1.3%
Other values (682) 403887
79.9%
ValueCountFrequency (%)
-159 2
< 0.1%
-158 1
< 0.1%
-156 1
< 0.1%
-154 1
< 0.1%
-153 2
< 0.1%
-152 2
< 0.1%
-151 1
< 0.1%
-150 1
< 0.1%
-149 1
< 0.1%
-147 1
< 0.1%
ValueCountFrequency (%)
601 1
 
< 0.1%
599 1
 
< 0.1%
598 2
< 0.1%
597 3
< 0.1%
595 2
< 0.1%
592 1
 
< 0.1%
591 1
 
< 0.1%
590 2
< 0.1%
589 3
< 0.1%
588 3
< 0.1%

Horizontal_Distance_To_Roadways
Real number (ℝ)

High correlation 

Distinct5785
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2431.98
Minimum0
Maximum7117
Zeros96
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T18:37:23.527720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile391
Q11140
median2078
Q33475
95-th percentile5571
Maximum7117
Range7117
Interquartile range (IQR)2335

Descriptive statistics

Standard deviation1600.4154
Coefficient of variation (CV)0.65807093
Kurtosis-0.53780115
Mean2431.98
Median Absolute Deviation (MAD)1085
Skewness0.65607631
Sum1.2290108 × 109
Variance2561329.4
MonotonicityNot monotonic
2025-06-09T18:37:23.654502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150 1078
 
0.2%
618 882
 
0.2%
900 821
 
0.2%
1020 805
 
0.2%
990 777
 
0.2%
960 764
 
0.2%
390 763
 
0.2%
1140 736
 
0.1%
1050 726
 
0.1%
750 725
 
0.1%
Other values (5775) 497277
98.4%
ValueCountFrequency (%)
0 96
 
< 0.1%
30 267
0.1%
42 153
 
< 0.1%
60 280
0.1%
67 249
< 0.1%
85 293
0.1%
90 331
0.1%
95 317
0.1%
108 537
0.1%
120 559
0.1%
ValueCountFrequency (%)
7117 1
< 0.1%
7116 1
< 0.1%
7112 1
< 0.1%
7097 1
< 0.1%
7092 1
< 0.1%
7087 2
< 0.1%
7082 1
< 0.1%
7079 1
< 0.1%
7078 2
< 0.1%
7069 1
< 0.1%

Hillshade_9am
Real number (ℝ)

High correlation 

Distinct207
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean212.28271
Minimum0
Maximum254
Zeros13
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T18:37:23.778020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile160
Q1199
median218
Q3231
95-th percentile245
Maximum254
Range254
Interquartile range (IQR)32

Descriptive statistics

Standard deviation26.629387
Coefficient of variation (CV)0.12544303
Kurtosis2.1182007
Mean212.28271
Median Absolute Deviation (MAD)15
Skewness-1.2439666
Sum1.0727792 × 108
Variance709.12424
MonotonicityNot monotonic
2025-06-09T18:37:23.892535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
226 10413
 
2.1%
228 10242
 
2.0%
224 10051
 
2.0%
230 10049
 
2.0%
223 9782
 
1.9%
222 9720
 
1.9%
233 9416
 
1.9%
227 9382
 
1.9%
225 9243
 
1.8%
221 9239
 
1.8%
Other values (197) 407817
80.7%
ValueCountFrequency (%)
0 13
< 0.1%
36 1
 
< 0.1%
46 2
 
< 0.1%
50 1
 
< 0.1%
52 1
 
< 0.1%
53 1
 
< 0.1%
54 3
 
< 0.1%
55 1
 
< 0.1%
56 5
 
< 0.1%
57 2
 
< 0.1%
ValueCountFrequency (%)
254 1641
 
0.3%
253 1822
 
0.4%
252 2123
0.4%
251 2409
0.5%
250 2766
0.5%
249 3116
0.6%
248 3249
0.6%
247 3691
0.7%
246 4069
0.8%
245 4560
0.9%

Hillshade_Noon
Real number (ℝ)

High correlation 

Distinct185
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean223.2563
Minimum0
Maximum254
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T18:37:24.029866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile187
Q1213
median226
Q3237
95-th percentile250
Maximum254
Range254
Interquartile range (IQR)24

Descriptive statistics

Standard deviation19.58077
Coefficient of variation (CV)0.087705341
Kurtosis2.4443385
Mean223.2563
Median Absolute Deviation (MAD)12
Skewness-1.1404263
Sum1.1282347 × 108
Variance383.40657
MonotonicityNot monotonic
2025-06-09T18:37:24.132382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
231 12314
 
2.4%
228 12230
 
2.4%
233 12063
 
2.4%
230 11975
 
2.4%
229 11849
 
2.3%
234 11805
 
2.3%
227 11601
 
2.3%
226 11594
 
2.3%
223 11564
 
2.3%
225 11495
 
2.3%
Other values (175) 386864
76.6%
ValueCountFrequency (%)
0 5
< 0.1%
30 1
 
< 0.1%
40 1
 
< 0.1%
42 1
 
< 0.1%
45 1
 
< 0.1%
53 2
 
< 0.1%
63 1
 
< 0.1%
64 1
 
< 0.1%
68 1
 
< 0.1%
71 1
 
< 0.1%
ValueCountFrequency (%)
254 3981
0.8%
253 4664
0.9%
252 5492
1.1%
251 5864
1.2%
250 6488
1.3%
249 6356
1.3%
248 6907
1.4%
247 7617
1.5%
246 7453
1.5%
245 7352
1.5%

Hillshade_3pm
Real number (ℝ)

High correlation 

Distinct255
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean142.57913
Minimum0
Maximum254
Zeros1266
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T18:37:24.226388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile79
Q1120
median143
Q3168
95-th percentile203
Maximum254
Range254
Interquartile range (IQR)48

Descriptive statistics

Standard deviation37.78155
Coefficient of variation (CV)0.26498653
Kurtosis0.54411121
Mean142.57913
Median Absolute Deviation (MAD)24
Skewness-0.29140078
Sum72052935
Variance1427.4455
MonotonicityNot monotonic
2025-06-09T18:37:24.313906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
143 6582
 
1.3%
145 6474
 
1.3%
138 6384
 
1.3%
146 6243
 
1.2%
142 6189
 
1.2%
136 6166
 
1.2%
139 6153
 
1.2%
149 6078
 
1.2%
135 6035
 
1.2%
150 6026
 
1.2%
Other values (245) 443024
87.7%
ValueCountFrequency (%)
0 1266
0.3%
1 14
 
< 0.1%
2 15
 
< 0.1%
3 15
 
< 0.1%
4 18
 
< 0.1%
5 18
 
< 0.1%
6 24
 
< 0.1%
7 28
 
< 0.1%
8 20
 
< 0.1%
9 31
 
< 0.1%
ValueCountFrequency (%)
254 4
 
< 0.1%
253 8
 
< 0.1%
252 16
 
< 0.1%
251 10
 
< 0.1%
250 15
 
< 0.1%
249 35
< 0.1%
248 41
< 0.1%
247 54
< 0.1%
246 68
< 0.1%
245 78
< 0.1%

Horizontal_Distance_To_Fire_Points
Real number (ℝ)

High correlation 

Distinct5827
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2010.1361
Minimum0
Maximum7173
Zeros45
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T18:37:24.397416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile408
Q11024
median1725
Q32592
95-th percentile5089
Maximum7173
Range7173
Interquartile range (IQR)1568

Descriptive statistics

Standard deviation1357.8179
Coefficient of variation (CV)0.67548557
Kurtosis1.5080032
Mean2010.1361
Median Absolute Deviation (MAD)771
Skewness1.266097
Sum1.0158303 × 109
Variance1843669.5
MonotonicityNot monotonic
2025-06-09T18:37:24.488712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
618 1244
 
0.2%
541 981
 
0.2%
607 930
 
0.2%
942 856
 
0.2%
997 844
 
0.2%
700 833
 
0.2%
726 798
 
0.2%
752 782
 
0.2%
900 774
 
0.2%
960 768
 
0.2%
Other values (5817) 496544
98.3%
ValueCountFrequency (%)
0 45
 
< 0.1%
30 184
< 0.1%
42 183
< 0.1%
60 182
< 0.1%
67 370
0.1%
85 183
< 0.1%
90 182
< 0.1%
95 366
0.1%
108 369
0.1%
120 180
< 0.1%
ValueCountFrequency (%)
7173 1
< 0.1%
7172 1
< 0.1%
7168 1
< 0.1%
7150 1
< 0.1%
7145 1
< 0.1%
7142 1
< 0.1%
7141 2
< 0.1%
7140 1
< 0.1%
7131 1
< 0.1%
7126 1
< 0.1%

Wilderness_Area_0
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
1.0
260796 
0.0
244558 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 260796
51.6%
0.0 244558
48.4%

Length

2025-06-09T18:37:24.567817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:37:24.616817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 260796
51.6%
0.0 244558
48.4%

Most occurring characters

ValueCountFrequency (%)
0 749912
49.5%
. 505354
33.3%
1 260796
 
17.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 749912
49.5%
. 505354
33.3%
1 260796
 
17.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 749912
49.5%
. 505354
33.3%
1 260796
 
17.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 749912
49.5%
. 505354
33.3%
1 260796
 
17.2%

Wilderness_Area_1
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
496925 
1.0
 
8429

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 496925
98.3%
1.0 8429
 
1.7%

Length

2025-06-09T18:37:24.665327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:37:24.708056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 496925
98.3%
1.0 8429
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 1002279
66.1%
. 505354
33.3%
1 8429
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1002279
66.1%
. 505354
33.3%
1 8429
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1002279
66.1%
. 505354
33.3%
1 8429
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1002279
66.1%
. 505354
33.3%
1 8429
 
0.6%

Wilderness_Area_2
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
306193 
1.0
199161 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 306193
60.6%
1.0 199161
39.4%

Length

2025-06-09T18:37:24.759578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:37:24.799579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 306193
60.6%
1.0 199161
39.4%

Most occurring characters

ValueCountFrequency (%)
0 811547
53.5%
. 505354
33.3%
1 199161
 
13.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 811547
53.5%
. 505354
33.3%
1 199161
 
13.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 811547
53.5%
. 505354
33.3%
1 199161
 
13.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 811547
53.5%
. 505354
33.3%
1 199161
 
13.1%

Wilderness_Area_3
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
468386 
1.0
 
36968

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 468386
92.7%
1.0 36968
 
7.3%

Length

2025-06-09T18:37:24.848581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:37:24.891088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 468386
92.7%
1.0 36968
 
7.3%

Most occurring characters

ValueCountFrequency (%)
0 973740
64.2%
. 505354
33.3%
1 36968
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 973740
64.2%
. 505354
33.3%
1 36968
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 973740
64.2%
. 505354
33.3%
1 36968
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 973740
64.2%
. 505354
33.3%
1 36968
 
2.4%

Soil_Type_0
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
502323 
1.0
 
3031

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 502323
99.4%
1.0 3031
 
0.6%

Length

2025-06-09T18:37:24.937842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:37:24.976359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 502323
99.4%
1.0 3031
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 1007677
66.5%
. 505354
33.3%
1 3031
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1007677
66.5%
. 505354
33.3%
1 3031
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1007677
66.5%
. 505354
33.3%
1 3031
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1007677
66.5%
. 505354
33.3%
1 3031
 
0.2%

Soil_Type_1
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
500436 
1.0
 
4918

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 500436
99.0%
1.0 4918
 
1.0%

Length

2025-06-09T18:37:25.026359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:37:25.065873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 500436
99.0%
1.0 4918
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0 1005790
66.3%
. 505354
33.3%
1 4918
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1005790
66.3%
. 505354
33.3%
1 4918
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1005790
66.3%
. 505354
33.3%
1 4918
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1005790
66.3%
. 505354
33.3%
1 4918
 
0.3%

Soil_Type_2
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
502823 
1.0
 
2531

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 502823
99.5%
1.0 2531
 
0.5%

Length

2025-06-09T18:37:25.112872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:37:25.152874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 502823
99.5%
1.0 2531
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 1008177
66.5%
. 505354
33.3%
1 2531
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1008177
66.5%
. 505354
33.3%
1 2531
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1008177
66.5%
. 505354
33.3%
1 2531
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1008177
66.5%
. 505354
33.3%
1 2531
 
0.2%

Soil_Type_3
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
497735 
1.0
 
7619

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 497735
98.5%
1.0 7619
 
1.5%

Length

2025-06-09T18:37:25.200703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:37:25.239707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 497735
98.5%
1.0 7619
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0 1003089
66.2%
. 505354
33.3%
1 7619
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1003089
66.2%
. 505354
33.3%
1 7619
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1003089
66.2%
. 505354
33.3%
1 7619
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1003089
66.2%
. 505354
33.3%
1 7619
 
0.5%

Soil_Type_4
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
503757 
1.0
 
1597

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 503757
99.7%
1.0 1597
 
0.3%

Length

2025-06-09T18:37:25.290216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:37:25.329218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 503757
99.7%
1.0 1597
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 1009111
66.6%
. 505354
33.3%
1 1597
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1009111
66.6%
. 505354
33.3%
1 1597
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1009111
66.6%
. 505354
33.3%
1 1597
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1009111
66.6%
. 505354
33.3%
1 1597
 
0.1%

Soil_Type_5
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
498779 
1.0
 
6575

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 498779
98.7%
1.0 6575
 
1.3%

Length

2025-06-09T18:37:25.375284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:37:25.414284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 498779
98.7%
1.0 6575
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 1004133
66.2%
. 505354
33.3%
1 6575
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1004133
66.2%
. 505354
33.3%
1 6575
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1004133
66.2%
. 505354
33.3%
1 6575
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1004133
66.2%
. 505354
33.3%
1 6575
 
0.4%

Soil_Type_6
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
505249 
1.0
 
105

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 505249
> 99.9%
1.0 105
 
< 0.1%

Length

2025-06-09T18:37:25.461509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:37:25.499509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 505249
> 99.9%
1.0 105
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 1010603
66.7%
. 505354
33.3%
1 105
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1010603
66.7%
. 505354
33.3%
1 105
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1010603
66.7%
. 505354
33.3%
1 105
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1010603
66.7%
. 505354
33.3%
1 105
 
< 0.1%

Soil_Type_7
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
505175 
1.0
 
179

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 505175
> 99.9%
1.0 179
 
< 0.1%

Length

2025-06-09T18:37:25.551099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:37:25.589604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 505175
> 99.9%
1.0 179
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 1010529
66.7%
. 505354
33.3%
1 179
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1010529
66.7%
. 505354
33.3%
1 179
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1010529
66.7%
. 505354
33.3%
1 179
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1010529
66.7%
. 505354
33.3%
1 179
 
< 0.1%

Soil_Type_8
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
504207 
1.0
 
1147

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 504207
99.8%
1.0 1147
 
0.2%

Length

2025-06-09T18:37:25.635604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:37:25.674120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 504207
99.8%
1.0 1147
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 1009561
66.6%
. 505354
33.3%
1 1147
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1009561
66.6%
. 505354
33.3%
1 1147
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1009561
66.6%
. 505354
33.3%
1 1147
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1009561
66.6%
. 505354
33.3%
1 1147
 
0.1%

Soil_Type_9
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
478425 
1.0
 
26929

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 478425
94.7%
1.0 26929
 
5.3%

Length

2025-06-09T18:37:25.721896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:37:25.764408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 478425
94.7%
1.0 26929
 
5.3%

Most occurring characters

ValueCountFrequency (%)
0 983779
64.9%
. 505354
33.3%
1 26929
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 983779
64.9%
. 505354
33.3%
1 26929
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 983779
64.9%
. 505354
33.3%
1 26929
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 983779
64.9%
. 505354
33.3%
1 26929
 
1.8%

Soil_Type_10
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
497100 
1.0
 
8254

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 497100
98.4%
1.0 8254
 
1.6%

Length

2025-06-09T18:37:25.810920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:37:25.849924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 497100
98.4%
1.0 8254
 
1.6%

Most occurring characters

ValueCountFrequency (%)
0 1002454
66.1%
. 505354
33.3%
1 8254
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1002454
66.1%
. 505354
33.3%
1 8254
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1002454
66.1%
. 505354
33.3%
1 8254
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1002454
66.1%
. 505354
33.3%
1 8254
 
0.5%

Soil_Type_11
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
475383 
1.0
 
29971

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 475383
94.1%
1.0 29971
 
5.9%

Length

2025-06-09T18:37:25.896081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:37:26.538298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 475383
94.1%
1.0 29971
 
5.9%

Most occurring characters

ValueCountFrequency (%)
0 980737
64.7%
. 505354
33.3%
1 29971
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 980737
64.7%
. 505354
33.3%
1 29971
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 980737
64.7%
. 505354
33.3%
1 29971
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 980737
64.7%
. 505354
33.3%
1 29971
 
2.0%

Soil_Type_12
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
492001 
1.0
 
13353

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 492001
97.4%
1.0 13353
 
2.6%

Length

2025-06-09T18:37:26.585411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:37:26.623026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 492001
97.4%
1.0 13353
 
2.6%

Most occurring characters

ValueCountFrequency (%)
0 997355
65.8%
. 505354
33.3%
1 13353
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 997355
65.8%
. 505354
33.3%
1 13353
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 997355
65.8%
. 505354
33.3%
1 13353
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 997355
65.8%
. 505354
33.3%
1 13353
 
0.9%

Soil_Type_13
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
504978 
1.0
 
376

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 504978
99.9%
1.0 376
 
0.1%

Length

2025-06-09T18:37:26.670544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:37:26.710546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 504978
99.9%
1.0 376
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1010332
66.6%
. 505354
33.3%
1 376
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1010332
66.6%
. 505354
33.3%
1 376
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1010332
66.6%
. 505354
33.3%
1 376
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1010332
66.6%
. 505354
33.3%
1 376
 
< 0.1%

Soil_Type_14
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
505351 
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 505351
> 99.9%
1.0 3
 
< 0.1%

Length

2025-06-09T18:37:26.756785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:37:26.794785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 505351
> 99.9%
1.0 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 1010705
66.7%
. 505354
33.3%
1 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1010705
66.7%
. 505354
33.3%
1 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1010705
66.7%
. 505354
33.3%
1 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1010705
66.7%
. 505354
33.3%
1 3
 
< 0.1%

Soil_Type_15
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
502813 
1.0
 
2541

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 502813
99.5%
1.0 2541
 
0.5%

Length

2025-06-09T18:37:26.840785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:37:26.879072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 502813
99.5%
1.0 2541
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 1008167
66.5%
. 505354
33.3%
1 2541
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1008167
66.5%
. 505354
33.3%
1 2541
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1008167
66.5%
. 505354
33.3%
1 2541
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1008167
66.5%
. 505354
33.3%
1 2541
 
0.2%

Soil_Type_16
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
502160 
1.0
 
3194

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 502160
99.4%
1.0 3194
 
0.6%

Length

2025-06-09T18:37:26.926074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:37:26.963583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 502160
99.4%
1.0 3194
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 1007514
66.5%
. 505354
33.3%
1 3194
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1007514
66.5%
. 505354
33.3%
1 3194
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1007514
66.5%
. 505354
33.3%
1 3194
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1007514
66.5%
. 505354
33.3%
1 3194
 
0.2%

Soil_Type_17
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
503525 
1.0
 
1829

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 503525
99.6%
1.0 1829
 
0.4%

Length

2025-06-09T18:37:27.010331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:37:27.048335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 503525
99.6%
1.0 1829
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 1008879
66.5%
. 505354
33.3%
1 1829
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1008879
66.5%
. 505354
33.3%
1 1829
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1008879
66.5%
. 505354
33.3%
1 1829
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1008879
66.5%
. 505354
33.3%
1 1829
 
0.1%

Soil_Type_18
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
501981 
1.0
 
3373

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 501981
99.3%
1.0 3373
 
0.7%

Length

2025-06-09T18:37:27.094447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:37:27.132447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 501981
99.3%
1.0 3373
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 1007335
66.4%
. 505354
33.3%
1 3373
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1007335
66.4%
. 505354
33.3%
1 3373
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1007335
66.4%
. 505354
33.3%
1 3373
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1007335
66.4%
. 505354
33.3%
1 3373
 
0.2%

Soil_Type_19
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
496711 
1.0
 
8643

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 496711
98.3%
1.0 8643
 
1.7%

Length

2025-06-09T18:37:27.179962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:37:27.217685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 496711
98.3%
1.0 8643
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 1002065
66.1%
. 505354
33.3%
1 8643
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1002065
66.1%
. 505354
33.3%
1 8643
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1002065
66.1%
. 505354
33.3%
1 8643
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1002065
66.1%
. 505354
33.3%
1 8643
 
0.6%

Soil_Type_20
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
504516 
1.0
 
838

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 504516
99.8%
1.0 838
 
0.2%

Length

2025-06-09T18:37:27.265196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:37:27.304193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 504516
99.8%
1.0 838
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 1009870
66.6%
. 505354
33.3%
1 838
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1009870
66.6%
. 505354
33.3%
1 838
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1009870
66.6%
. 505354
33.3%
1 838
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1009870
66.6%
. 505354
33.3%
1 838
 
0.1%

Soil_Type_21
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
479672 
1.0
 
25682

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 479672
94.9%
1.0 25682
 
5.1%

Length

2025-06-09T18:37:27.352199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:37:27.390710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 479672
94.9%
1.0 25682
 
5.1%

Most occurring characters

ValueCountFrequency (%)
0 985026
65.0%
. 505354
33.3%
1 25682
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 985026
65.0%
. 505354
33.3%
1 25682
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 985026
65.0%
. 505354
33.3%
1 25682
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 985026
65.0%
. 505354
33.3%
1 25682
 
1.7%

Soil_Type_22
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
456668 
1.0
48686 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 456668
90.4%
1.0 48686
 
9.6%

Length

2025-06-09T18:37:27.435710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:37:27.476011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 456668
90.4%
1.0 48686
 
9.6%

Most occurring characters

ValueCountFrequency (%)
0 962022
63.5%
. 505354
33.3%
1 48686
 
3.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 962022
63.5%
. 505354
33.3%
1 48686
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 962022
63.5%
. 505354
33.3%
1 48686
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 962022
63.5%
. 505354
33.3%
1 48686
 
3.2%

Soil_Type_23
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
488395 
1.0
 
16959

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 488395
96.6%
1.0 16959
 
3.4%

Length

2025-06-09T18:37:27.524011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:37:27.566128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 488395
96.6%
1.0 16959
 
3.4%

Most occurring characters

ValueCountFrequency (%)
0 993749
65.5%
. 505354
33.3%
1 16959
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 993749
65.5%
. 505354
33.3%
1 16959
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 993749
65.5%
. 505354
33.3%
1 16959
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 993749
65.5%
. 505354
33.3%
1 16959
 
1.1%

Soil_Type_24
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
505353 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 505353
> 99.9%
1.0 1
 
< 0.1%

Length

2025-06-09T18:37:27.614133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:37:27.651135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 505353
> 99.9%
1.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 1010707
66.7%
. 505354
33.3%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1010707
66.7%
. 505354
33.3%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1010707
66.7%
. 505354
33.3%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1010707
66.7%
. 505354
33.3%
1 1
 
< 0.1%

Soil_Type_25
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
503040 
1.0
 
2314

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 503040
99.5%
1.0 2314
 
0.5%

Length

2025-06-09T18:37:27.700643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:37:27.738363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 503040
99.5%
1.0 2314
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 1008394
66.5%
. 505354
33.3%
1 2314
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1008394
66.5%
. 505354
33.3%
1 2314
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1008394
66.5%
. 505354
33.3%
1 2314
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1008394
66.5%
. 505354
33.3%
1 2314
 
0.2%

Soil_Type_26
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
504801 
1.0
 
553

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 504801
99.9%
1.0 553
 
0.1%

Length

2025-06-09T18:37:27.786825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:37:27.826823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 504801
99.9%
1.0 553
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1010155
66.6%
. 505354
33.3%
1 553
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1010155
66.6%
. 505354
33.3%
1 553
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1010155
66.6%
. 505354
33.3%
1 553
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1010155
66.6%
. 505354
33.3%
1 553
 
< 0.1%

Soil_Type_27
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
504408 
1.0
 
946

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 504408
99.8%
1.0 946
 
0.2%

Length

2025-06-09T18:37:27.873340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:37:27.910340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 504408
99.8%
1.0 946
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 1009762
66.6%
. 505354
33.3%
1 946
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1009762
66.6%
. 505354
33.3%
1 946
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1009762
66.6%
. 505354
33.3%
1 946
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1009762
66.6%
. 505354
33.3%
1 946
 
0.1%

Soil_Type_28
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
390180 
1.0
115174 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 390180
77.2%
1.0 115174
 
22.8%

Length

2025-06-09T18:37:27.956344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:37:27.996612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 390180
77.2%
1.0 115174
 
22.8%

Most occurring characters

ValueCountFrequency (%)
0 895534
59.1%
. 505354
33.3%
1 115174
 
7.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 895534
59.1%
. 505354
33.3%
1 115174
 
7.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 895534
59.1%
. 505354
33.3%
1 115174
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 895534
59.1%
. 505354
33.3%
1 115174
 
7.6%

Soil_Type_29
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
475184 
1.0
 
30170

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 475184
94.0%
1.0 30170
 
6.0%

Length

2025-06-09T18:37:28.047616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:37:28.087127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 475184
94.0%
1.0 30170
 
6.0%

Most occurring characters

ValueCountFrequency (%)
0 980538
64.7%
. 505354
33.3%
1 30170
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 980538
64.7%
. 505354
33.3%
1 30170
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 980538
64.7%
. 505354
33.3%
1 30170
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 980538
64.7%
. 505354
33.3%
1 30170
 
2.0%

Soil_Type_30
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
481101 
1.0
 
24253

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 481101
95.2%
1.0 24253
 
4.8%

Length

2025-06-09T18:37:28.133130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:37:28.171637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 481101
95.2%
1.0 24253
 
4.8%

Most occurring characters

ValueCountFrequency (%)
0 986455
65.1%
. 505354
33.3%
1 24253
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 986455
65.1%
. 505354
33.3%
1 24253
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 986455
65.1%
. 505354
33.3%
1 24253
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 986455
65.1%
. 505354
33.3%
1 24253
 
1.6%

Soil_Type_31
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
458914 
1.0
46440 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 458914
90.8%
1.0 46440
 
9.2%

Length

2025-06-09T18:37:28.218637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:37:28.256357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 458914
90.8%
1.0 46440
 
9.2%

Most occurring characters

ValueCountFrequency (%)
0 964268
63.6%
. 505354
33.3%
1 46440
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 964268
63.6%
. 505354
33.3%
1 46440
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 964268
63.6%
. 505354
33.3%
1 46440
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 964268
63.6%
. 505354
33.3%
1 46440
 
3.1%

Soil_Type_32
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
471457 
1.0
 
33897

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 471457
93.3%
1.0 33897
 
6.7%

Length

2025-06-09T18:37:28.305867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:37:28.343867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 471457
93.3%
1.0 33897
 
6.7%

Most occurring characters

ValueCountFrequency (%)
0 976811
64.4%
. 505354
33.3%
1 33897
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 976811
64.4%
. 505354
33.3%
1 33897
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 976811
64.4%
. 505354
33.3%
1 33897
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 976811
64.4%
. 505354
33.3%
1 33897
 
2.2%

Soil_Type_33
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
504648 
1.0
 
706

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 504648
99.9%
1.0 706
 
0.1%

Length

2025-06-09T18:37:28.390384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:37:28.429381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 504648
99.9%
1.0 706
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1010002
66.6%
. 505354
33.3%
1 706
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1010002
66.6%
. 505354
33.3%
1 706
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1010002
66.6%
. 505354
33.3%
1 706
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1010002
66.6%
. 505354
33.3%
1 706
 
< 0.1%

Soil_Type_34
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
503963 
1.0
 
1391

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 503963
99.7%
1.0 1391
 
0.3%

Length

2025-06-09T18:37:28.475892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:37:28.513704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 503963
99.7%
1.0 1391
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 1009317
66.6%
. 505354
33.3%
1 1391
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1009317
66.6%
. 505354
33.3%
1 1391
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1009317
66.6%
. 505354
33.3%
1 1391
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1009317
66.6%
. 505354
33.3%
1 1391
 
0.1%

Soil_Type_35
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
505235 
1.0
 
119

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 505235
> 99.9%
1.0 119
 
< 0.1%

Length

2025-06-09T18:37:28.565219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:37:28.606845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 505235
> 99.9%
1.0 119
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 1010589
66.7%
. 505354
33.3%
1 119
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1010589
66.7%
. 505354
33.3%
1 119
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1010589
66.7%
. 505354
33.3%
1 119
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1010589
66.7%
. 505354
33.3%
1 119
 
< 0.1%

Soil_Type_36
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
505056 
1.0
 
298

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 505056
99.9%
1.0 298
 
0.1%

Length

2025-06-09T18:37:28.651849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:37:28.690058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 505056
99.9%
1.0 298
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1010410
66.6%
. 505354
33.3%
1 298
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1010410
66.6%
. 505354
33.3%
1 298
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1010410
66.6%
. 505354
33.3%
1 298
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1010410
66.6%
. 505354
33.3%
1 298
 
< 0.1%

Soil_Type_37
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
491835 
1.0
 
13519

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 491835
97.3%
1.0 13519
 
2.7%

Length

2025-06-09T18:37:28.736790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:37:28.773804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 491835
97.3%
1.0 13519
 
2.7%

Most occurring characters

ValueCountFrequency (%)
0 997189
65.8%
. 505354
33.3%
1 13519
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 997189
65.8%
. 505354
33.3%
1 13519
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 997189
65.8%
. 505354
33.3%
1 13519
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 997189
65.8%
. 505354
33.3%
1 13519
 
0.9%

Soil_Type_38
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
494948 
1.0
 
10406

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 494948
97.9%
1.0 10406
 
2.1%

Length

2025-06-09T18:37:28.821805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:37:28.860060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 494948
97.9%
1.0 10406
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 1000302
66.0%
. 505354
33.3%
1 10406
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1000302
66.0%
. 505354
33.3%
1 10406
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1000302
66.0%
. 505354
33.3%
1 10406
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1000302
66.0%
. 505354
33.3%
1 10406
 
0.7%

Soil_Type_39
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
498520 
1.0
 
6834

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 498520
98.6%
1.0 6834
 
1.4%

Length

2025-06-09T18:37:28.907059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:37:28.947062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 498520
98.6%
1.0 6834
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0 1003874
66.2%
. 505354
33.3%
1 6834
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1003874
66.2%
. 505354
33.3%
1 6834
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1003874
66.2%
. 505354
33.3%
1 6834
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1003874
66.2%
. 505354
33.3%
1 6834
 
0.5%

Cover_Type
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0589349
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T18:37:28.980573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q32
95-th percentile6
Maximum7
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3893957
Coefficient of variation (CV)0.67481283
Kurtosis4.9724135
Mean2.0589349
Median Absolute Deviation (MAD)0
Skewness2.2809236
Sum1040491
Variance1.9304204
MonotonicityNot monotonic
2025-06-09T18:37:29.033358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 254165
50.3%
1 178709
35.4%
3 28058
 
5.6%
7 17532
 
3.5%
6 14851
 
2.9%
5 9292
 
1.8%
4 2747
 
0.5%
ValueCountFrequency (%)
1 178709
35.4%
2 254165
50.3%
3 28058
 
5.6%
4 2747
 
0.5%
5 9292
 
1.8%
6 14851
 
2.9%
7 17532
 
3.5%
ValueCountFrequency (%)
7 17532
 
3.5%
6 14851
 
2.9%
5 9292
 
1.8%
4 2747
 
0.5%
3 28058
 
5.6%
2 254165
50.3%
1 178709
35.4%

Distance_to_Water
Real number (ℝ)

High correlation  Zeros 

Distinct48761
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean272.713
Minimum0
Maximum1418.9168
Zeros21630
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T18:37:29.101876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30
Q1108.16654
median225.85836
Q3390.10383
95-th percentile689.3271
Maximum1418.9168
Range1418.9168
Interquartile range (IQR)281.93729

Descriptive statistics

Standard deviation215.62566
Coefficient of variation (CV)0.7906688
Kurtosis1.604842
Mean272.713
Median Absolute Deviation (MAD)135.58655
Skewness1.1832935
Sum1.3781661 × 108
Variance46494.427
MonotonicityNot monotonic
2025-06-09T18:37:29.183389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 21630
 
4.3%
30 4151
 
0.8%
30.14962686 3374
 
0.7%
30.01666204 3338
 
0.7%
30.06659276 3333
 
0.7%
30.2654919 2748
 
0.5%
30.41381265 2308
 
0.5%
30.59411708 2177
 
0.4%
30.8058436 1810
 
0.4%
31.04834939 1299
 
0.3%
Other values (48751) 459186
90.9%
ValueCountFrequency (%)
0 21630
4.3%
30 4151
 
0.8%
30.01666204 3338
 
0.7%
30.06659276 3333
 
0.7%
30.14962686 3374
 
0.7%
30.2654919 2748
 
0.5%
30.41381265 2308
 
0.5%
30.59411708 2177
 
0.4%
30.8058436 1810
 
0.4%
31.04834939 1299
 
0.3%
ValueCountFrequency (%)
1418.91684 1
< 0.1%
1413.295794 1
< 0.1%
1411.059531 1
< 0.1%
1407.459058 1
< 0.1%
1397.781456 1
< 0.1%
1395.054838 1
< 0.1%
1394.460469 1
< 0.1%
1390.893598 1
< 0.1%
1389.705724 1
< 0.1%
1384.234084 1
< 0.1%

Avg_Hillshade
Real number (ℝ)

High correlation 

Distinct384
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean192.70605
Minimum31.666667
Maximum213.66667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T18:37:29.265709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum31.666667
5-th percentile165.33333
Q1185.66667
median195.33333
Q3202.66667
95-th percentile211
Maximum213.66667
Range182
Interquartile range (IQR)17

Descriptive statistics

Standard deviation14.393874
Coefficient of variation (CV)0.074693421
Kurtosis3.0701699
Mean192.70605
Median Absolute Deviation (MAD)8.3333333
Skewness-1.3616708
Sum97384772
Variance207.18361
MonotonicityNot monotonic
2025-06-09T18:37:29.351713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
199.3333333 6583
 
1.3%
198 6186
 
1.2%
198.6666667 6168
 
1.2%
201.3333333 5970
 
1.2%
196.3333333 5963
 
1.2%
200 5952
 
1.2%
195.6666667 5878
 
1.2%
195.3333333 5798
 
1.1%
196.6666667 5781
 
1.1%
200.6666667 5696
 
1.1%
Other values (374) 445379
88.1%
ValueCountFrequency (%)
31.66666667 1
< 0.1%
34.33333333 1
< 0.1%
55.33333333 1
< 0.1%
59.66666667 1
< 0.1%
61.66666667 2
< 0.1%
63.66666667 1
< 0.1%
64.33333333 1
< 0.1%
70 1
< 0.1%
73.33333333 1
< 0.1%
76 1
< 0.1%
ValueCountFrequency (%)
213.6666667 247
 
< 0.1%
213.3333333 1927
0.4%
213 2939
0.6%
212.6666667 3181
0.6%
212.3333333 3415
0.7%
212 3115
0.6%
211.6666667 3447
0.7%
211.3333333 3739
0.7%
211 3372
0.7%
210.6666667 4121
0.8%

Hydro_Road_Fire_Distance
Real number (ℝ)

High correlation 

Distinct12679
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4708.2733
Minimum108
Maximum13141
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T18:37:29.449751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum108
5-th percentile1448
Q12800
median4270
Q36223
95-th percentile9595
Maximum13141
Range13033
Interquartile range (IQR)3423

Descriptive statistics

Standard deviation2478.0415
Coefficient of variation (CV)0.52631642
Kurtosis-0.029145393
Mean4708.2733
Median Absolute Deviation (MAD)1624
Skewness0.7353494
Sum2.3793447 × 109
Variance6140689.8
MonotonicityNot monotonic
2025-06-09T18:37:29.534977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2950 134
 
< 0.1%
3001 125
 
< 0.1%
2956 123
 
< 0.1%
2914 122
 
< 0.1%
5340 121
 
< 0.1%
3118 118
 
< 0.1%
3020 117
 
< 0.1%
3547 116
 
< 0.1%
3194 116
 
< 0.1%
2149 116
 
< 0.1%
Other values (12669) 504146
99.8%
ValueCountFrequency (%)
108 1
< 0.1%
115 1
< 0.1%
125 1
< 0.1%
150 2
< 0.1%
152 1
< 0.1%
157 1
< 0.1%
162 1
< 0.1%
164 1
< 0.1%
166 2
< 0.1%
180 1
< 0.1%
ValueCountFrequency (%)
13141 1
< 0.1%
13134 1
< 0.1%
13127 1
< 0.1%
13124 1
< 0.1%
13121 1
< 0.1%
13114 1
< 0.1%
13113 1
< 0.1%
13111 1
< 0.1%
13110 2
< 0.1%
13109 2
< 0.1%

Elevation_x_Slope
Real number (ℝ)

High correlation 

Distinct37825
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40100.483
Minimum0
Maximum204880
Zeros621
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T18:37:29.626099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11242.6
Q124696
median37308
Q352513
95-th percentile78532
Maximum204880
Range204880
Interquartile range (IQR)27817

Descriptive statistics

Standard deviation20984.36
Coefficient of variation (CV)0.52329443
Kurtosis0.91042054
Mean40100.483
Median Absolute Deviation (MAD)13658
Skewness0.80115176
Sum2.026494 × 1010
Variance4.4034335 × 108
MonotonicityNot monotonic
2025-06-09T18:37:29.709348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 621
 
0.1%
38376 163
 
< 0.1%
35508 159
 
< 0.1%
39312 157
 
< 0.1%
29520 154
 
< 0.1%
29780 146
 
< 0.1%
38844 146
 
< 0.1%
26748 143
 
< 0.1%
29590 143
 
< 0.1%
48960 142
 
< 0.1%
Other values (37815) 503380
99.6%
ValueCountFrequency (%)
0 621
0.1%
1929 1
 
< 0.1%
1940 1
 
< 0.1%
2012 1
 
< 0.1%
2088 2
 
< 0.1%
2100 1
 
< 0.1%
2103 1
 
< 0.1%
2121 1
 
< 0.1%
2122 1
 
< 0.1%
2129 1
 
< 0.1%
ValueCountFrequency (%)
204880 1
< 0.1%
203808 1
< 0.1%
201110 1
< 0.1%
195796 1
< 0.1%
193579 1
< 0.1%
189297 1
< 0.1%
185673 1
< 0.1%
184912 1
< 0.1%
179312 1
< 0.1%
177320 1
< 0.1%

Interactions

2025-06-09T18:37:16.906789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:36:53.346543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:36:55.071278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:36:56.581955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:36:58.191501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:36:59.756421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:37:01.773321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:37:03.302556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:37:04.759602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:37:06.214162image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:37:07.758418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:37:09.466923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-06-09T18:37:10.616971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:37:12.848179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:37:14.624726image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:37:16.341031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:37:18.157822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:36:54.619329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:36:56.169536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:36:57.721966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:36:59.343365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:37:01.363210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:37:02.895312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:37:04.369798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:37:05.828197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:37:07.336537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:37:08.990215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:37:10.730794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:37:12.966436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:37:14.742782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:37:16.453657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:37:18.272337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:36:54.734363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:36:56.270043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:36:57.855996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:36:59.445283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:37:01.465715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:37:02.998388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:37:04.469309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:37:05.926597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:37:07.445148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:37:09.104059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:37:10.843940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:37:13.086245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:37:14.855804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:37:16.570458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:37:18.387593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:36:54.855688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:36:56.378611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:36:57.992112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:36:59.550348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:37:01.567545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:37:03.100897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:37:04.566573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:37:06.022429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:37:07.548658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:37:09.219353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:37:10.959768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:37:13.207525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:37:14.970878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:37:16.682979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:37:18.503774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:36:54.968953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:36:56.481702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:36:58.096238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:36:59.658311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:37:01.676069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:37:03.202407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:37:04.666086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:37:06.119653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:37:07.658898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:37:09.346383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:37:11.074611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:37:13.325040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:37:15.087694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:37:16.794281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-06-09T18:37:29.828647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AspectAvg_HillshadeCover_TypeDistance_to_WaterElevationElevation_x_SlopeHillshade_3pmHillshade_9amHillshade_NoonHorizontal_Distance_To_Fire_PointsHorizontal_Distance_To_HydrologyHorizontal_Distance_To_RoadwaysHydro_Road_Fire_DistanceSlopeSoil_Type_0Soil_Type_1Soil_Type_10Soil_Type_11Soil_Type_12Soil_Type_13Soil_Type_14Soil_Type_15Soil_Type_16Soil_Type_17Soil_Type_18Soil_Type_19Soil_Type_2Soil_Type_20Soil_Type_21Soil_Type_22Soil_Type_23Soil_Type_24Soil_Type_25Soil_Type_26Soil_Type_27Soil_Type_28Soil_Type_29Soil_Type_3Soil_Type_30Soil_Type_31Soil_Type_32Soil_Type_33Soil_Type_34Soil_Type_35Soil_Type_36Soil_Type_37Soil_Type_38Soil_Type_39Soil_Type_4Soil_Type_5Soil_Type_6Soil_Type_7Soil_Type_8Soil_Type_9Vertical_Distance_To_HydrologyWilderness_Area_0Wilderness_Area_1Wilderness_Area_2Wilderness_Area_3
Aspect1.0000.4360.0400.0030.0320.0700.632-0.4150.414-0.110-0.0030.019-0.0470.0680.0530.0460.0700.0990.1460.0180.0040.0220.0190.0490.0130.0460.1030.0520.0210.0340.1540.0000.0700.0660.0730.1110.1440.1540.0950.1060.0890.0320.0270.0410.0290.0450.0390.0270.0270.0250.0070.0040.0320.1800.0720.2260.1170.1980.136
Avg_Hillshade0.4361.000-0.0780.0250.193-0.4990.655-0.1940.9860.0180.0360.2190.158-0.5230.1100.0200.0710.0870.0700.0080.0030.0180.0340.0220.0550.0340.0780.0340.0450.1430.1230.0010.0260.0150.0070.0740.0630.0450.0580.1270.1100.0210.0090.0150.0260.0490.0650.0450.0800.0140.0080.0150.0130.213-0.1090.0850.0640.0380.230
Cover_Type0.040-0.0781.0000.002-0.4930.084-0.028-0.009-0.063-0.121-0.007-0.226-0.2230.1670.2500.2930.1070.2050.1370.1640.0140.0300.1950.0630.0450.0450.4170.0520.2010.1970.0700.0000.0510.0220.0380.2060.1470.2580.0820.1020.0870.0340.1810.0420.1280.3630.3340.2220.1810.3150.0140.0100.0340.5140.1210.3450.0760.1620.817
Distance_to_Water0.0030.0250.0021.0000.2510.0790.036-0.0470.0220.0650.9990.0620.1430.0350.0330.0480.0410.0810.0160.0390.0000.0760.0940.0210.0500.1010.0280.0510.0620.1910.0410.0000.0240.1760.0370.0790.0540.0350.0690.1400.0720.1600.0440.0660.0190.0680.0440.1950.0180.0410.0200.0110.0270.0600.6470.1180.0250.1690.098
Elevation0.0320.193-0.4930.2511.000-0.0230.0730.0260.1860.1290.2610.4170.368-0.1750.3650.2250.1720.2860.1360.1300.0160.0760.1360.2310.0630.0880.2670.0260.2160.1770.0870.0010.0690.0530.0660.2340.1220.1890.0920.1870.0870.0500.1300.0470.0780.4090.3390.6280.2890.3420.0200.0270.1820.5470.0600.2880.1450.2220.926
Elevation_x_Slope0.070-0.4990.0840.079-0.0231.000-0.181-0.119-0.443-0.1320.057-0.155-0.1780.9840.0650.0440.0750.1950.2090.0100.0000.0440.0600.0670.0970.0810.0610.0250.0410.1930.1160.0070.0430.0770.0940.0630.0980.0770.0790.1160.2350.0140.0080.0140.0190.0390.1810.0950.0450.0380.0230.0360.0440.1720.3310.1890.0590.1400.147
Hillshade_3pm0.6320.655-0.0280.0360.073-0.1811.000-0.8190.566-0.0830.0350.1050.030-0.1870.1590.0210.0750.1440.2230.0060.0000.0360.0370.0520.0580.0670.1650.0500.0440.1410.0300.0070.0330.0790.1820.1000.1480.0330.0580.1320.1100.0170.0260.0130.0260.0640.0950.0450.0650.0200.0120.0170.0280.1530.0360.1990.1020.1260.208
Hillshade_9am-0.415-0.194-0.009-0.0470.026-0.119-0.8191.000-0.0860.126-0.0390.0070.067-0.1250.0520.0350.0450.1240.1390.0100.0000.0250.0240.0370.0560.0560.0880.0340.0490.1240.1230.0000.0380.0570.1590.0910.1510.0440.0880.1050.0970.0120.0260.0090.0120.0470.0580.0190.0760.0280.0120.0180.0270.307-0.1320.2310.0810.1340.278
Hillshade_Noon0.4140.986-0.0630.0220.186-0.4430.566-0.0861.0000.0200.0320.2090.152-0.4680.0940.0250.0740.0850.0690.0110.0010.0160.0300.0230.0480.0370.0480.0340.0390.1320.1350.0000.0300.0240.0080.0840.0460.0700.0550.1260.1170.0230.0080.0170.0260.0470.0590.0460.0840.0170.0070.0150.0140.235-0.0990.1030.0470.0640.236
Horizontal_Distance_To_Fire_Points-0.1100.018-0.1210.0650.129-0.132-0.0830.1260.0201.0000.0740.3710.735-0.1690.1240.0880.0760.2990.1160.0450.0040.1100.0430.1710.0270.1290.0780.0330.0810.0900.0900.0060.0800.0400.0420.2220.0740.0680.0830.1330.0910.0360.0290.0230.0380.0870.0560.0760.0700.1160.0880.0450.0560.217-0.0390.4070.1300.2830.340
Horizontal_Distance_To_Hydrology-0.0030.036-0.0070.9990.2610.0570.035-0.0390.0320.0741.0000.0720.1540.0130.0370.0470.0420.0810.0200.0390.0000.0750.0930.0190.0500.1000.0310.0510.0590.1870.0450.0000.0250.1630.0370.0790.0550.0380.0690.1430.0670.1630.0430.0700.0190.0700.0420.1870.0170.0430.0210.0110.0270.0670.6240.1130.0250.1650.107
Horizontal_Distance_To_Roadways0.0190.219-0.2260.0620.417-0.1550.1050.0070.2090.3710.0721.0000.880-0.2280.1460.1030.1250.1040.1450.0330.0050.0570.0650.0800.1050.0870.0850.0410.1500.0680.0770.0000.0920.0440.0770.3210.1160.1030.1610.1830.1340.0440.0490.0350.0500.1090.1110.0910.1100.1550.0550.0480.0680.238-0.0320.4830.1680.4220.398
Hydro_Road_Fire_Distance-0.0470.158-0.2230.1430.368-0.1780.0300.0670.1520.7350.1540.8801.000-0.2470.2100.1300.1020.2160.1520.0580.0100.0870.0660.1120.0740.1320.1280.0520.1110.0970.0810.0000.1020.0570.0520.3250.0950.1000.1180.2190.1250.0470.0310.0230.0580.1210.0960.0900.1330.1950.0650.0590.0990.3200.0020.5160.1750.4200.543
Slope0.068-0.5230.1670.035-0.1750.984-0.187-0.125-0.468-0.1690.013-0.228-0.2471.0000.1350.0190.0650.1760.2050.0040.0000.0340.0410.0460.1030.0760.1170.0260.0640.2060.1120.0040.0450.0550.0980.0900.1000.1070.0860.1410.2250.0150.0170.0170.0110.0690.0930.0420.0940.0150.0210.0360.0340.2650.3160.2160.0720.1270.311
Soil_Type_00.0530.1100.2500.0330.3650.0650.1590.0520.0940.1240.0370.1460.2100.1351.0000.0070.0100.0190.0130.0010.0000.0050.0060.0040.0060.0100.0050.0020.0180.0250.0140.0000.0050.0020.0030.0420.0190.0090.0170.0250.0210.0020.0040.0000.0000.0130.0110.0090.0040.0090.0000.0000.0030.0180.0260.0800.0100.0630.276
Soil_Type_10.0460.0200.2930.0480.2250.0440.0210.0350.0250.0880.0470.1030.1300.0190.0071.0000.0130.0250.0160.0020.0000.0070.0080.0060.0080.0130.0070.0040.0230.0320.0180.0000.0060.0030.0040.0540.0250.0120.0220.0310.0270.0030.0050.0000.0010.0160.0140.0110.0050.0110.0000.0000.0040.0230.0420.1020.0130.0340.138
Soil_Type_100.0700.0710.1070.0410.1720.0750.0750.0450.0740.0760.0420.1250.1020.0650.0100.0131.0000.0320.0210.0030.0000.0090.0100.0080.0100.0170.0090.0050.0300.0420.0240.0000.0090.0040.0050.0700.0320.0160.0290.0410.0340.0040.0060.0000.0020.0210.0190.0150.0070.0150.0000.0010.0060.0310.0220.1330.0170.1410.000
Soil_Type_110.0990.0870.2050.0810.2860.1950.1440.1240.0850.2990.0810.1040.2160.1760.0190.0250.0321.0000.0410.0070.0000.0180.0200.0150.0200.0330.0180.0100.0580.0820.0470.0000.0170.0080.0110.1360.0630.0310.0560.0800.0670.0090.0130.0030.0060.0420.0360.0290.0140.0290.0030.0040.0120.0600.0740.2430.0330.2020.071
Soil_Type_120.1460.0700.1370.0160.1360.2090.2230.1390.0690.1160.0200.1450.1520.2050.0130.0160.0210.0411.0000.0040.0000.0120.0130.0100.0130.0220.0120.0060.0380.0540.0310.0000.0110.0050.0070.0890.0410.0200.0370.0520.0440.0060.0080.0020.0030.0270.0240.0190.0090.0190.0010.0020.0080.0390.1060.1700.0190.2040.046
Soil_Type_130.0180.0080.1640.0390.1300.0100.0060.0100.0110.0450.0390.0330.0580.0040.0010.0020.0030.0070.0041.0000.0000.0000.0010.0000.0010.0030.0000.0000.0060.0090.0050.0000.0000.0000.0000.0150.0070.0030.0060.0080.0070.0000.0000.0000.0000.0040.0030.0030.0000.0020.0000.0000.0000.0060.0150.0280.0030.0190.092
Soil_Type_140.0040.0030.0140.0000.0160.0000.0000.0000.0010.0040.0000.0050.0100.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0010.0000.0000.007
Soil_Type_150.0220.0180.0300.0760.0760.0440.0360.0250.0160.1100.0750.0570.0870.0340.0050.0070.0090.0180.0120.0000.0001.0000.0050.0040.0050.0090.0050.0020.0160.0230.0130.0000.0040.0010.0020.0390.0180.0090.0160.0230.0190.0020.0030.0000.0000.0120.0100.0080.0030.0080.0000.0000.0030.0170.0420.0460.0070.0500.008
Soil_Type_160.0190.0340.1950.0940.1360.0600.0370.0240.0300.0430.0930.0650.0660.0410.0060.0080.0100.0200.0130.0010.0000.0051.0000.0040.0060.0100.0050.0030.0180.0260.0150.0000.0050.0020.0030.0430.0200.0100.0180.0250.0210.0020.0040.0000.0000.0130.0110.0090.0040.0090.0000.0000.0030.0190.0440.0820.0100.0580.054
Soil_Type_170.0490.0220.0630.0210.2310.0670.0520.0370.0230.1710.0190.0800.1120.0460.0040.0060.0080.0150.0100.0000.0000.0040.0041.0000.0050.0080.0040.0010.0140.0200.0110.0000.0040.0010.0020.0330.0150.0070.0130.0190.0160.0010.0020.0000.0000.0100.0090.0070.0030.0070.0000.0000.0020.0140.0350.0580.0080.0490.017
Soil_Type_180.0130.0550.0450.0500.0630.0970.0580.0560.0480.0270.0500.1050.0740.1030.0060.0080.0100.0200.0130.0010.0000.0050.0060.0051.0000.0110.0050.0030.0190.0270.0150.0000.0050.0020.0030.0440.0210.0100.0180.0260.0220.0020.0040.0000.0000.0130.0120.0090.0040.0090.0000.0000.0030.0190.0420.0490.0130.0410.023
Soil_Type_190.0460.0340.0450.1010.0880.0810.0670.0560.0370.1290.1000.0870.1320.0760.0100.0130.0170.0330.0220.0030.0000.0090.0100.0080.0111.0000.0090.0050.0300.0430.0240.0000.0090.0040.0050.0720.0330.0160.0300.0420.0350.0050.0070.0010.0030.0220.0190.0150.0070.0150.0000.0020.0060.0310.0610.0700.0170.0470.037
Soil_Type_20.1030.0780.4170.0280.2670.0610.1650.0880.0480.0780.0310.0850.1280.1170.0050.0070.0090.0180.0120.0000.0000.0050.0050.0040.0050.0091.0000.0020.0160.0230.0130.0000.0040.0010.0020.0380.0180.0090.0160.0220.0190.0020.0030.0000.0000.0120.0100.0080.0030.0080.0000.0000.0030.0170.0330.0730.0090.0530.244
Soil_Type_200.0520.0340.0520.0510.0260.0250.0500.0340.0340.0330.0510.0410.0520.0260.0020.0040.0050.0100.0060.0000.0000.0020.0030.0010.0030.0050.0021.0000.0090.0130.0070.0000.0020.0000.0000.0220.0100.0050.0090.0130.0110.0000.0010.0000.0000.0060.0060.0040.0010.0040.0000.0000.0000.0090.0240.0420.0050.0500.011
Soil_Type_210.0210.0450.2010.0620.2160.0410.0440.0490.0390.0810.0590.1500.1110.0640.0180.0230.0300.0580.0380.0060.0000.0160.0180.0140.0190.0300.0160.0091.0000.0760.0430.0000.0160.0070.0100.1260.0580.0290.0520.0740.0620.0080.0120.0030.0050.0380.0330.0270.0130.0260.0030.0040.0110.0550.0830.1150.1040.1110.065
Soil_Type_220.0340.1430.1970.1910.1770.1930.1410.1240.1320.0900.1870.0680.0970.2060.0250.0320.0420.0820.0540.0090.0000.0230.0260.0200.0270.0430.0230.0130.0761.0000.0610.0000.0220.0110.0140.1770.0820.0400.0730.1040.0880.0120.0170.0050.0080.0540.0470.0380.0180.0370.0040.0060.0150.0770.1530.0460.0950.0230.092
Soil_Type_230.1540.1230.0700.0410.0870.1160.0300.1230.1350.0900.0450.0770.0810.1120.0140.0180.0240.0470.0310.0050.0000.0130.0150.0110.0150.0240.0130.0070.0430.0611.0000.0000.0120.0060.0080.1010.0470.0230.0420.0590.0500.0070.0100.0020.0040.0310.0270.0220.0100.0210.0020.0030.0090.0440.0440.1290.0870.1370.052
Soil_Type_240.0000.0010.0000.0000.0010.0070.0070.0000.0000.0060.0000.0000.0000.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0000.0050.0000.000
Soil_Type_250.0700.0260.0510.0240.0690.0430.0330.0380.0300.0800.0250.0920.1020.0450.0050.0060.0090.0170.0110.0000.0000.0040.0050.0040.0050.0090.0040.0020.0160.0220.0120.0001.0000.0010.0020.0370.0170.0080.0150.0210.0180.0020.0030.0000.0000.0110.0100.0080.0030.0080.0000.0000.0030.0160.0190.0700.0090.0840.019
Soil_Type_260.0660.0150.0220.1760.0530.0770.0790.0570.0240.0400.1630.0440.0570.0550.0020.0030.0040.0080.0050.0000.0000.0010.0020.0010.0020.0040.0010.0000.0070.0110.0060.0000.0011.0000.0000.0180.0080.0040.0070.0100.0090.0000.0000.0000.0000.0050.0040.0030.0000.0030.0000.0000.0000.0080.1780.0340.0040.0410.009
Soil_Type_270.0730.0070.0380.0370.0660.0940.1820.1590.0080.0420.0370.0770.0520.0980.0030.0040.0050.0110.0070.0000.0000.0020.0030.0020.0030.0050.0020.0000.0100.0140.0080.0000.0020.0001.0000.0230.0110.0050.0100.0140.0110.0000.0010.0000.0000.0070.0060.0050.0010.0050.0000.0000.0010.0100.1250.0450.0050.0540.012
Soil_Type_280.1110.0740.2060.0790.2340.0630.1000.0910.0840.2220.0790.3210.3250.0900.0420.0540.0700.1360.0890.0150.0000.0390.0430.0330.0440.0720.0380.0220.1260.1770.1010.0000.0370.0180.0231.0000.1370.0670.1220.1730.1460.0200.0280.0080.0130.0900.0790.0640.0310.0620.0080.0100.0260.1290.0960.5260.0710.4380.153
Soil_Type_290.1440.0630.1470.0540.1220.0980.1480.1510.0460.0740.0550.1160.0950.1000.0190.0250.0320.0630.0410.0070.0000.0180.0200.0150.0210.0330.0180.0100.0580.0820.0470.0000.0170.0080.0110.1371.0000.0310.0570.0800.0680.0090.0130.0030.0060.0420.0360.0290.0140.0290.0030.0040.0120.0600.0270.2440.0330.2030.071
Soil_Type_30.1540.0450.2580.0350.1890.0770.0330.0440.0700.0680.0380.1030.1000.1070.0090.0120.0160.0310.0200.0030.0000.0090.0100.0070.0100.0160.0090.0050.0290.0400.0230.0000.0080.0040.0050.0670.0311.0000.0280.0390.0330.0040.0060.0000.0020.0200.0180.0140.0070.0140.0000.0010.0060.0290.0250.1280.0160.1120.042
Soil_Type_300.0950.0580.0820.0690.0920.0790.0580.0880.0550.0830.0690.1610.1180.0860.0170.0220.0290.0560.0370.0060.0000.0160.0180.0130.0180.0300.0160.0090.0520.0730.0420.0000.0150.0070.0100.1220.0570.0281.0000.0710.0600.0080.0120.0030.0050.0370.0320.0260.0120.0260.0030.0040.0110.0530.0410.2320.0290.2780.063
Soil_Type_310.1060.1270.1020.1400.1870.1160.1320.1050.1260.1330.1430.1830.2190.1410.0250.0310.0410.0800.0520.0080.0000.0230.0250.0190.0260.0420.0220.0130.0740.1040.0590.0000.0210.0100.0140.1730.0800.0390.0711.0000.0850.0120.0170.0040.0070.0530.0460.0370.0180.0360.0040.0060.0150.0750.0490.3280.0410.3730.089
Soil_Type_320.0890.1100.0870.0720.0870.2350.1100.0970.1170.0910.0670.1340.1250.2250.0210.0270.0340.0670.0440.0070.0000.0190.0210.0160.0220.0350.0190.0110.0620.0880.0500.0000.0180.0090.0110.1460.0680.0330.0600.0851.0000.0100.0140.0040.0060.0440.0390.0310.0150.0310.0030.0050.0130.0640.1450.2770.0170.3280.075
Soil_Type_330.0320.0210.0340.1600.0500.0140.0170.0120.0230.0360.1630.0440.0470.0150.0020.0030.0040.0090.0060.0000.0000.0020.0020.0010.0020.0050.0020.0000.0080.0120.0070.0000.0020.0000.0000.0200.0090.0040.0080.0120.0101.0000.0000.0000.0000.0060.0050.0040.0010.0040.0000.0000.0000.0090.1030.0390.0040.0460.010
Soil_Type_340.0270.0090.1810.0440.1300.0080.0260.0260.0080.0290.0430.0490.0310.0170.0040.0050.0060.0130.0080.0000.0000.0030.0040.0020.0040.0070.0030.0010.0120.0170.0100.0000.0030.0000.0010.0280.0130.0060.0120.0170.0140.0001.0000.0000.0000.0080.0070.0060.0020.0060.0000.0000.0020.0120.0280.0040.0050.0140.015
Soil_Type_350.0410.0150.0420.0660.0470.0140.0130.0090.0170.0230.0700.0350.0230.0170.0000.0000.0000.0030.0020.0000.0000.0000.0000.0000.0000.0010.0000.0000.0030.0050.0020.0000.0000.0000.0000.0080.0030.0000.0030.0040.0040.0000.0001.0000.0000.0020.0010.0000.0000.0000.0000.0000.0000.0030.0250.0160.0010.0190.004
Soil_Type_360.0290.0260.1280.0190.0780.0190.0260.0120.0260.0380.0190.0500.0580.0110.0000.0010.0020.0060.0030.0000.0000.0000.0000.0000.0000.0030.0000.0000.0050.0080.0040.0000.0000.0000.0000.0130.0060.0020.0050.0070.0060.0000.0000.0001.0000.0040.0030.0020.0000.0020.0000.0000.0000.0050.0060.0130.0020.0080.007
Soil_Type_370.0450.0490.3630.0680.4090.0390.0640.0470.0470.0870.0700.1090.1210.0690.0130.0160.0210.0420.0270.0040.0000.0120.0130.0100.0130.0220.0120.0060.0380.0540.0310.0000.0110.0050.0070.0900.0420.0200.0370.0530.0440.0060.0080.0020.0041.0000.0240.0190.0090.0190.0010.0020.0080.0390.0240.0130.0140.0150.047
Soil_Type_380.0390.0650.3340.0440.3390.1810.0950.0580.0590.0560.0420.1110.0960.0930.0110.0140.0190.0360.0240.0030.0000.0100.0110.0090.0120.0190.0100.0060.0330.0470.0270.0000.0100.0040.0060.0790.0360.0180.0320.0460.0390.0050.0070.0010.0030.0241.0000.0170.0080.0170.0010.0020.0070.0340.0550.0390.0150.0140.041
Soil_Type_390.0270.0450.2220.1950.6280.0950.0450.0190.0460.0760.1870.0910.0900.0420.0090.0110.0150.0290.0190.0030.0000.0080.0090.0070.0090.0150.0080.0040.0270.0380.0220.0000.0080.0030.0050.0640.0290.0140.0260.0370.0310.0040.0060.0000.0020.0190.0171.0000.0060.0130.0000.0010.0050.0280.2410.0280.0490.0240.033
Soil_Type_40.0270.0800.1810.0180.2890.0450.0650.0760.0840.0700.0170.1100.1330.0940.0040.0050.0070.0140.0090.0000.0000.0030.0040.0030.0040.0070.0030.0010.0130.0180.0100.0000.0030.0000.0010.0310.0140.0070.0120.0180.0150.0010.0020.0000.0000.0090.0080.0061.0000.0060.0000.0000.0020.0130.0360.0580.0070.0450.200
Soil_Type_50.0250.0140.3150.0410.3420.0380.0200.0280.0170.1160.0430.1550.1950.0150.0090.0110.0150.0290.0190.0020.0000.0080.0090.0070.0090.0150.0080.0040.0260.0370.0210.0000.0080.0030.0050.0620.0290.0140.0260.0360.0310.0040.0060.0000.0020.0190.0170.0130.0061.0000.0000.0010.0050.0270.0550.1190.0150.0930.409
Soil_Type_60.0070.0080.0140.0200.0200.0230.0120.0120.0070.0880.0210.0550.0650.0210.0000.0000.0000.0030.0010.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.0040.0020.0000.0000.0000.0000.0080.0030.0000.0030.0040.0030.0000.0000.0000.0000.0010.0010.0000.0000.0001.0000.0000.0000.0030.0080.0140.0000.0110.004
Soil_Type_70.0040.0150.0100.0110.0270.0360.0170.0180.0150.0450.0110.0480.0590.0360.0000.0000.0010.0040.0020.0000.0000.0000.0000.0000.0000.0020.0000.0000.0040.0060.0030.0000.0000.0000.0000.0100.0040.0010.0040.0060.0050.0000.0000.0000.0000.0020.0020.0010.0000.0010.0001.0000.0000.0040.0110.0180.0010.0150.005
Soil_Type_80.0320.0130.0340.0270.1820.0440.0280.0270.0140.0560.0270.0680.0990.0340.0030.0040.0060.0120.0080.0000.0000.0030.0030.0020.0030.0060.0030.0000.0110.0150.0090.0000.0030.0000.0010.0260.0120.0060.0110.0150.0130.0000.0020.0000.0000.0080.0070.0050.0020.0050.0000.0001.0000.0110.0310.0460.0060.0380.013
Soil_Type_90.1800.2130.5140.0600.5470.1720.1530.3070.2350.2170.0670.2380.3200.2650.0180.0230.0310.0600.0390.0060.0000.0170.0190.0140.0190.0310.0170.0090.0550.0770.0440.0000.0160.0080.0100.1290.0600.0290.0530.0750.0640.0090.0120.0030.0050.0390.0340.0280.0130.0270.0030.0040.0111.0000.0900.2450.0310.0290.539
Vertical_Distance_To_Hydrology0.072-0.1090.1210.6470.0600.3310.036-0.132-0.099-0.0390.624-0.0320.0020.3160.0260.0420.0220.0740.1060.0150.0000.0420.0440.0350.0420.0610.0330.0240.0830.1530.0440.0050.0190.1780.1250.0960.0270.0250.0410.0490.1450.1030.0280.0250.0060.0240.0550.2410.0360.0550.0080.0110.0310.0901.0000.1940.0680.1660.106
Wilderness_Area_00.2260.0850.3450.1180.2880.1890.1990.2310.1030.4070.1130.4830.5160.2160.0800.1020.1330.2430.1700.0280.0010.0460.0820.0580.0490.0700.0730.0420.1150.0460.1290.0000.0700.0340.0450.5260.2440.1280.2320.3280.2770.0390.0040.0160.0130.0130.0390.0280.0580.1190.0140.0180.0460.2450.1941.0000.1340.8330.290
Wilderness_Area_10.1170.0640.0760.0250.1450.0590.1020.0810.0470.1300.0250.1680.1750.0720.0100.0130.0170.0330.0190.0030.0000.0070.0100.0080.0130.0170.0090.0050.1040.0950.0870.0050.0090.0040.0050.0710.0330.0160.0290.0410.0170.0040.0050.0010.0020.0140.0150.0490.0070.0150.0000.0010.0060.0310.0680.1341.0000.1050.037
Wilderness_Area_20.1980.0380.1620.1690.2220.1400.1260.1340.0640.2830.1650.4220.4200.1270.0630.0340.1410.2020.2040.0190.0000.0500.0580.0490.0410.0470.0530.0500.1110.0230.1370.0000.0840.0410.0540.4380.2030.1120.2780.3730.3280.0460.0140.0190.0080.0150.0140.0240.0450.0930.0110.0150.0380.0290.1660.8330.1051.0000.227
Wilderness_Area_30.1360.2300.8170.0980.9260.1470.2080.2780.2360.3400.1070.3980.5430.3110.2760.1380.0000.0710.0460.0920.0070.0080.0540.0170.0230.0370.2440.0110.0650.0920.0520.0000.0190.0090.0120.1530.0710.0420.0630.0890.0750.0100.0150.0040.0070.0470.0410.0330.2000.4090.0040.0050.0130.5390.1060.2900.0370.2271.000

Missing values

2025-06-09T18:37:18.711134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-09T18:37:19.934868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

ElevationAspectSlopeHorizontal_Distance_To_HydrologyVertical_Distance_To_HydrologyHorizontal_Distance_To_RoadwaysHillshade_9amHillshade_NoonHillshade_3pmHorizontal_Distance_To_Fire_PointsWilderness_Area_0Wilderness_Area_1Wilderness_Area_2Wilderness_Area_3Soil_Type_0Soil_Type_1Soil_Type_2Soil_Type_3Soil_Type_4Soil_Type_5Soil_Type_6Soil_Type_7Soil_Type_8Soil_Type_9Soil_Type_10Soil_Type_11Soil_Type_12Soil_Type_13Soil_Type_14Soil_Type_15Soil_Type_16Soil_Type_17Soil_Type_18Soil_Type_19Soil_Type_20Soil_Type_21Soil_Type_22Soil_Type_23Soil_Type_24Soil_Type_25Soil_Type_26Soil_Type_27Soil_Type_28Soil_Type_29Soil_Type_30Soil_Type_31Soil_Type_32Soil_Type_33Soil_Type_34Soil_Type_35Soil_Type_36Soil_Type_37Soil_Type_38Soil_Type_39Cover_TypeDistance_to_WaterAvg_HillshadeHydro_Road_Fire_DistanceElevation_x_Slope
02596.051.03.0258.00.0510.0221.0232.0148.06279.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.05258.000200.3337047.07788.0
12590.056.02.0212.0-6.0390.0220.0235.0151.06225.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.05212.085202.0006827.05180.0
22804.0139.09.0268.065.03180.0234.0238.0135.06121.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.02275.770202.3339569.025236.0
32785.0155.018.0242.0118.03090.0238.0238.0122.06211.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.02269.236199.3339543.050130.0
42595.045.02.0153.0-1.0391.0220.0234.0150.06172.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.05153.003201.3336716.05190.0
52579.0132.06.0300.0-15.067.0230.0237.0140.06031.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.02300.375202.3336398.015474.0
62606.045.07.0270.05.0633.0222.0225.0138.06256.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.05270.046195.0007159.018242.0
72605.049.04.0234.07.0573.0222.0230.0144.06228.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.05234.105198.6677035.010420.0
82617.045.09.0240.056.0666.0223.0221.0133.06244.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.05246.447192.3337150.023553.0
92612.059.010.0247.011.0636.0228.0219.0124.06230.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.05247.245190.3337113.026120.0
ElevationAspectSlopeHorizontal_Distance_To_HydrologyVertical_Distance_To_HydrologyHorizontal_Distance_To_RoadwaysHillshade_9amHillshade_NoonHillshade_3pmHorizontal_Distance_To_Fire_PointsWilderness_Area_0Wilderness_Area_1Wilderness_Area_2Wilderness_Area_3Soil_Type_0Soil_Type_1Soil_Type_2Soil_Type_3Soil_Type_4Soil_Type_5Soil_Type_6Soil_Type_7Soil_Type_8Soil_Type_9Soil_Type_10Soil_Type_11Soil_Type_12Soil_Type_13Soil_Type_14Soil_Type_15Soil_Type_16Soil_Type_17Soil_Type_18Soil_Type_19Soil_Type_20Soil_Type_21Soil_Type_22Soil_Type_23Soil_Type_24Soil_Type_25Soil_Type_26Soil_Type_27Soil_Type_28Soil_Type_29Soil_Type_30Soil_Type_31Soil_Type_32Soil_Type_33Soil_Type_34Soil_Type_35Soil_Type_36Soil_Type_37Soil_Type_38Soil_Type_39Cover_TypeDistance_to_WaterAvg_HillshadeHydro_Road_Fire_DistanceElevation_x_Slope
5053443190.056.012.0190.014.01597.0228.0214.0117.01584.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.01190.515186.3333371.038280.0
5053453183.060.016.0162.07.01595.0231.0205.0102.01608.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.01162.151179.3333365.050928.0
5053463175.053.017.0134.020.01593.0227.0203.0104.01632.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.01135.484178.0003359.053975.0
5053473169.044.015.0108.014.01591.0222.0205.0114.01657.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.01108.904180.3333356.047535.0
5053483164.048.014.085.09.01590.0224.0209.0116.01681.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.0185.475183.0003356.044296.0
5053493158.061.013.060.013.01590.0230.0211.0111.01706.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.0161.392184.0003356.041054.0
5053503151.068.013.030.06.01590.0233.0214.0111.01731.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.0130.594186.0003351.040963.0
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